摘要
红外图像具有整体亮度偏暗、对比度较低、目标与背景区分不明显的特点。因此,在对红外图像进行分析之前,必须先对图像质量进行改善。传统的基于模糊集理论的Pal.King算法,在增强红外图像对比度的同时,丢失部分细节信息。在分析这一问题产生原因的基础上,结合图像反色和多分辨率图像融合等理论,提出了一种新的基于模糊集理论的图像增强算法,新的算法不仅能够提高红外图像的对比度,而且能很好的突出图像中不同层次的灰度信息和边缘信息,最重要是它能保持原始图像的细节信息。
Infrared images have the features of dark and low contrast. And it is hard to distinguish between target and background. So, it is necessary to improved image quality before analysis it. The images processed by tradi- tional Pal. King＇ s algorithm which based on fuzzy set theory, loss a lot of details. It is analysied the reason of the problem, then combined with the theory of image negative transformation and multi-resolution image fusion. A new image enhancement algorithm based on fuzzy set theory was proposed. The algorithm not only can improve the contrast of infrared images, but also can prominent image＇ new algorithm can keep details of original images. s edge and gray information. The most important is that the
出处
《黑龙江大学自然科学学报》
CAS
北大核心
2012年第3期401-406,共6页
Journal of Natural Science of Heilongjiang University
基金
国家自然科学基金资助项目(61077079)
教育部博士点计划基金资助项目(20102304110013)
关键词
图像增强
模糊集理论
细节信息
反色
图像融合
对比度
image enhancement
fuzzy set theory
details information
negative transformation
image fusion
Contrast
